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Related Experiment Videos

Diagrammatic dataset on AI-generated formative feedback for XML-based UML models.

Janka Pecuchová1, Ľubomír Benko1, Martin Drlík1

  • 1Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 949 01 Nitra, Slovakia.

Data in Brief
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...

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This dataset offers student UML models and AI feedback in Slovak, enabling research on automated assessment and large language model performance in non-English software engineering education.

Area of Science:

  • Computer Science Education
  • Artificial Intelligence in Education
  • Software Engineering Pedagogy

Background:

  • Educational datasets are crucial for advancing AI in learning.
  • Software Engineering education benefits from automated feedback systems.
  • Multilingual applications of AI in education require specific datasets.

Purpose of the Study:

  • To release a novel dataset of student-generated UML models and AI-generated formative feedback.
  • To facilitate research on automated formative assessment in software engineering.
  • To enable studies on the effectiveness of large language models in non-English educational contexts.

Main Methods:

  • Collected and anonymized 112 student records, 448 feedback records, and 700 XML reports.
Keywords:
AI feedback generationAutomated assessmentLarge language modelsMultimodal datasetPrompt-based evaluationSoftware engineering educationUML diagram

Related Experiment Videos

  • Utilized Enterprise Architect (v16) for XML model generation.
  • Employed OpenAI's GPT-4-Turbo API for generating Slovak-language formative feedback.

    • Main Results:

    • The dataset links UML models, AI feedback, scores, grades, and human evaluations.
    • Provides insights into AI feedback generation in Slovak for technical disciplines.
    • Offers a reproducible basis for benchmarking and extending research in software engineering education.

    Conclusions:

    • The dataset supports research on automated formative assessment and prompt engineering.
    • Facilitates human-AI feedback comparison and multilingual feedback analysis.
    • Highlights the potential of LLMs in diverse educational settings and languages.